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Illumination estimation via random vector functional link based on improved arithmetic optimization algorithm

COLOR RESEARCH AND APPLICATION 2022年第3期47卷 644-656页

作者： Liu, Xiangqi Zhou, Zhiyu Hangzhou Dianzi Univ Coll Mech Engn Informat Engn Sch Hangzhou Peoples R China Hangzhou Dianzi Univ Sch Mech Engn Hangzhou Peoples R China Zhejiang Sci Tech Univ Sch Informat Sci & Technol Hangzhou Peoples R China

In order to solve the problem that the existing illumination estimation methods have low accuracy in image restoration, a random vector functional link illumination estimation algorithm based on whale optimization arithmetic algorithm is proposed in this article. First, in order to improve the optimization accuracy of arithmetic optimization algorithm, the whale optimization algorithm is applied to optimize the initial population of the arithmetic optimization algorithm. Then, we utilize the improved arithmetic optimization algorithm to improve the input weight and hidden layer offset of random vector functional link, and exclude the uncertainty caused by the random generation of random vector functional link's input weight and hidden layer bias. Finally, the proposed whale arithmetic optimization algorithm-random vector functional link model is applied to SFU lab dataset and Gehler-Shi dataset for illumination estimation. Compared with the other earlier illumination estimation algorithms, the proposed illumination estimation model in this article has better effect and higher stability. In addition, due to the diagonal mapping method for color correction and image restoration, the proposed illumination estimation algorithm can also get better results in image restoration.

关键词： arithmetic optimization algorithm illumination estimation random vector functional link whale optimization algorithm

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A hybrid machine learning forecasting model for photovoltaic power

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ENERGY REPORTS 2024年 11卷 5125-5138页

作者： Hou, Zhijian Zhang, Yunhui Liu, Qian Ye, Xiaojiang Shenzhen Polytech Univ Sch Mech & Elect Engn Shenzhen 518055 Peoples R China Wuhan Inst Technol Sch Opt Informat & Energy Engn Wuhan 430205 Peoples R China

The increasing use of photovoltaic (PV) power generation presents a significant opportunity for global energy transformation. However, accurately forecasting PV power remains a challenge. This study proposes a hybrid approach that combines variational mode decomposition (VMD), whale optimization algorithm (WOA), and long short -term memory neural network (LSTM) to forecast photovoltaic (PV) power accurately. The model decomposes the time series of PV power using VMD to address the non-stationary nature of the time series. The VMD parameters are optimized using WOA. Subsequently, and the PV power time series data is then decomposed using the optimized VMD parameters to obtain multiple intrinsic mode functions (IMFs) components and a residual component. These components are then reconstructed with meteorological parameters to obtain the reconstructed IMF components and residuals. Finally, multiple LSTM sub-models are built, with each of them taking the IMF components and residual from the previous reconstruction as inputs. The sub-models are optimized using the WOA method to determine their hyperparameters and then constructed with these optimized hyperparameters. The predicted values of each IMF and residual are output and added for sequence reconstruction to derive the final predicted value of PV power. The model 's effectiveness was verified for one-hourahead forecasting at the 1.8 MW solar system in Yulara, central Australia. The test results show that the proposed model outperforms other benchmark models, with mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and R-squared value (R 2 ) of 15.247 kW, 19.753 kW, 4.405 % and 0.997, respectively. Compared to the LSTM, MAE, RMSE, and MAPE decreased by 84.942 %, 86.746 %, and 82.611 %, respectively, while R 2 increased by 23.438 %. The proposed model has better predictive performance for both stable power changes and large fluctuations, essential for effectively integratin

关键词： PV power forecasting Variational mode decomposition whale optimization algorithm Long short -term memory neural network

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Remaining useful life prediction of lithium-ion batteries based on performance degradation mechanism analysis and improved Deep Extreme Learning Machine model

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IONICS 2024年第9期30卷 5845-5852页

作者： Feng, Renjun Wang, Shunli Yu, Chunmei Fernandez, Carlos Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621010 Peoples R China Sichuan Univ Sch Elect Engn Chengdu 610065 Peoples R China Robert Gordon Univ Sch Pharm & Life Sci Aberdeen Scotland

The remaining useful life (RUL) of lithium-ion batteries is a decisive factor in the stability of electric vehicle systems. Aiming at the problem of limited robustness of Deep Extreme Learning Machine (DELM) in lithium-ion battery RUL prediction, an improved whale optimization algorithm (IWOA) is proposed to improve the prediction ability of DELM. Four health features are extracted from the battery aging data, the outliers in the feature data are detected and removed using Hampel filtering, and the health features are dimensionality reduced using principal component analysis to avoid data overfitting. Then, chaotic tent mapping, positive cosine algorithm, and chaotic adaptive inertia weights are used to improve the whale optimization algorithm and increase the search diversity. The introduction of IWOA to optimize the parameter selection of the DELM model effectively solves the problems of low efficiency and poor stability of parameter selection. The method was fully validated using the cycle battery dataset and the prediction results were compared with the conventional method. The results show that the IWOA-DELM method has small prediction errors, strong state tracking fitting ability, good generalization ability, and robustness.

关键词： Lithium-ion battery Remaining usable life whale optimization algorithm Hampel filter Deep Extreme Learning Machine

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Forecasting Crude Oil Prices: Evidence From WOA-VMD-FE-Transformer Model

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COMPUTATIONAL ECONOMICS 2025年 1-32页

作者： Yu, Liang Zhang, Xi Lin, Yu Yu, Yuanyuan Wu, Jingyu Dai, Dongsheng Chengdu Univ Technol Sch Business 1 Dongsanlu Chengdu 610059 Sichuan Peoples R China Chengdu Univ Technol Sch Management Sci Chengdu 610059 Peoples R China

The wild fluctuations in crude oil prices in recent years have increased the urgent need for accurate price estimates. A reliable method for crude oil price forecasting is essential to guide production and investment. Therefore, this paper proposes a new crude oil futures price series decomposition and reconstruction prediction model called WOA-VMD-FE-Transformer model. Firstly, the parameters of the variational mode decomposition (VMD) method are optimized by using the whale optimization algorithm (WOA), and then the optimized WOA-VMD method is used to decompose the crude oil futures price series into multiple sub-sequences. Then, according to the fuzzy entropy (FE) value of the sub-sequence, it is reorganized into three subsequences: low frequency, medium frequency and high frequency. Finally, we trained these three subsequences using the Transformer model and applied them to the test set to make predictions. By adding the forecasts together, we get the final forecast and use multiple metrics to assess the accuracy of the forecast. After experimental verification, WOA-VMD-FE-Transformer model shows high accuracy in predicting crude oil futures prices. The application of the model presented in this study can help decision-makers develop strategies, help investors make informed investment decisions, and also promote the equilibrium between energy supply and demand in the market. Hence, the research holds considerable importance in fostering the sustainable growth of the energy market and related industries.

关键词： Crude oil futures forecast Variational modal decomposition whale optimization algorithm Fuzzy entropy Transformer

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Enhancing Mine Blasting Safety: Developing Intelligent Systems for Accurate Flyrock Prediction through Optimized Group Method of Data Handling Methods

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NATURAL RESOURCES RESEARCH 2025年第2期34卷 1199-1218页

作者： Ding, Xiaohua Hasanipanah, Mahdi Monjezi, Masoud Abdullah, Rini Asnida Nguyen, Tung Ulrikh, Dmitrii Vladimirovich China Univ Min & Technol Sch Mines Xuzhou 221116 Peoples R China China Univ Min & Technol State Key Lab Coal Resources & Safe Min Xuzhou 221116 Peoples R China Duy Tan Univ Inst Res & Dev Da Nang Vietnam Duy Tan Univ Sch Engn & Technol Da Nang Vietnam Tarbiat Modares Univ Fac Engn Tehran Iran Univ Teknol Malaysia Fac Civil Engn Johor Baharu Malaysia St Petersburg State Univ Architecture & Civil Engn Dept Water Use & Environm 4 2nd Krasnoarmeiskaya Str St Petersburg 190005 Russia

Flyrock, the unintended projection of rocks during mining blasts, poses significant safety risks and potential damage. Predicting flyrock is essential for implementing safety measures, minimizing injuries, preventing equipment and structural damage, optimizing blast plans, reducing downtime, and saving costs. Accurate predictions mitigate hazards, improve operational efficiency, and ensure the safety of workers and surrounding infrastructure. This study explored and developed hybrid methods for predicting flyrock using the group method of data handling (GMDH). Four swarm-based algorithms-particle swarm optimization (PSO), artificial bee colony (ABC), ant colony optimization (ACO), and whale optimization algorithm (WOA)-were combined with GMDH to enhance prediction accuracy. Additionally, a k-fold cross-validation method was applied to the datasets to improve reliability. The accuracy of these methods was evaluated using various statistical functions, such as Nash-Sutcliffe coefficient and Willmott's index, along with R-squared correlation (R2) graphs, half-violin plots, and quantile-quantile plots. The R2 values for the WOA-GMDH, ACO-GMDH, ABC-GMDH, and PSO-GMDH models were 0.99, 0.97, 0.96, and 0.96, respectively. The WOA-GMDH method yielded the most accurate results, demonstrating superior performance when combined with GMDH. Furthermore, the performance of the WOA-GMDH model was compared with models developed in the literature using the same database, confirming its effectiveness. Sensitivity analysis identified that, in WOA-GMDH modeling, the powder factor as the most significant parameter while the spacing parameter was the least significant. The ACO-GMDH method exhibited the narrowest uncertainty band;whereas, the PSO-GMDH method had the widest, indicating the highest level of uncertainty.

关键词： Surface mines Flyrock Group method of data handling whale optimization algorithm

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A hybrid WOA-CNN-BiLSTM framework with enhanced accuracy for low-voltage shunt capacitor remaining life prediction in power systems

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ENERGY 2025年 326卷

作者： Li, Ningning Xu, Weiyao Zeng, Qiuyu Ren, Yanjie Ma, Wenchuan Tan, Kezhu Northeast Agr Univ Sch Elect & Informat Engn Harbin 150030 Heilongjiang Peoples R China

Low-voltage shunt capacitors, as a good reactive power compensation component, have been widely used in power systems. However, when their capacitance decays to a threshold value, causing them to fail, it will seriously affect the safe operation of the system. This paper aims to study the remaining service life of lowvoltage shunt capacitors and establish a data-based prediction model considering various environmental factors. Based on the traditional long short-term memory neural network prediction, an improved bidirectional long short-term memory network method combining convolutional neural networks and whale optimization algorithm is proposed, which improves the accuracy, speed, and robustness of prediction. The root mean square error (RMSE) and mean absolute error (MAE) before and after optimization are compared based on simulation. The simulation results show that compared with the traditional LSTM model, the RMSE of the prediction results of the WOA-CNN-BiLSTM model is reduced by 0.0117, and the MAE is reduced by ***, the WOA-CNNBiLSTM model has higher accuracy and stability, can effectively reduce the power quality decline caused by the abnormal working state of the reactive power compensation equipment, so as to improve the operating efficiency of each equipment in the power system and extend its service life.

关键词： Low-voltage shunt capacitor Remaining service life Bidirectional long short-term memory neural network whale optimization algorithm

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Deep learning models for multi-step prediction of water levels incorporating meteorological variables and historical data

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2024年 1-23页

作者： Chen, Lingxuan Wang, Zhaocai Jiang, Ziang Lin, Xiaolong South China Univ Technol Sch Elect Power Engn Guangzhou 510641 Guangdong Peoples R China Shanghai Ocean Univ Coll Informat Hucheng Huan Rd 999 Shanghai 201306 Peoples R China Shanghai Ocean Univ Coll Engn Sci & Technol Shanghai 201306 Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China

Precise multi-step water level predictions are crucial for managing water resources and mitigating the effects of extreme weather. This study introduces a novel approach by integrating Variational Mode Decomposition (VMD), whale optimization algorithm (WOA), and Long Short-Term Memory (LSTM) to forecast variations in water levels, employing both endogenous and exogenous environmental variables. Furthermore, this research proposes two additional fusion algorithms, each possessing unique potential for enhancement: Multivariate Long Short-Term Memory (MLSTM) and an advancement in the Residual Sequence (RESID). The predictive accuracy of these diverse algorithms is assessed using data from the water levels in Jinan Baotu Spring, China. The findings indicate that the VMD-WOA-LSTM model presents the most robust results for both long-term and short-term predictions. For multi-step, ultra-short-term forecasts, VMD-WOA-MLSTM proves to be a pragmatic algorithm. However, the refined algorithm that incorporates RESID does not significantly improve and, indeed, may diminish prediction accuracy. Conclusively, the VMD-WOA-LSTM, exemplifying a data-driven predictive algorithm, boasts high accuracy and demonstrates versatility in water level forecasting across various scenarios.

关键词： Groundwater level prediction Long and short-term memory Spring water level whale optimization algorithm Variational modal decomposition

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Design and performance of 3D-Printed ABS@rGO/CF/CeO2 composites for microwave absorption and mechanical strength

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CHEMICAL ENGINEERING JOURNAL 2024年 499卷

作者： Dong, Huaiyu Gao, Shuailong Yu, Chen Wang, Zhichen Zhang, Haofeng Huang, Yixing Zhao, Tian Li, Ying Beijing Inst Technol Inst Adv Struct Technol Beijing Key Lab Lightweight Multifunct Composite M Beijing 100081 Peoples R China

Traditional microwave absorbing materials struggle to balance wideband absorption with mechanical properties, particularly in multifunctional applications. This study explores the effects of short carbon fibers (CF), cerium dioxide (CeO2), and reduced graphene oxide (rGO) on the electromagnetic and mechanical properties of 3Dprinted composites. The ABS@CF/CeO2/rGO composite with a thickness of 2.2 mm exhibited exceptional microwave absorption, achieving an effective absorption bandwidth (EAB) of 6.6 GHz and a minimum reflection loss (RLmin) of -40.9 dB. The whale optimization algorithm (WOA) was employed to design a load-bearing, microwave-absorbing superstructure, which significantly improved wideband absorption. Experimental results confirmed the superstructure's isotropy and wide-angle absorption capabilities. The calculated electromagnetic parameters were applied to evaluate the radar cross-section (RCS) of large targets, demonstrating enhanced stealth performance for unmanned aerial vehicles (UAVs). Mechanical tests revealed that rGO improved interlayer bonding strength, while CF negatively impacted bending performance. The superstructure also showed excellent mechanical stability in load-bearing tests, underscoring its potential for practical engineering applications. This study offers insights into designing advanced composite materials with balanced electromagnetic and mechanical properties, guiding future multifunctional material development.

关键词： Microwave absorbing materials 3D Printing whale optimization algorithm Equivalent electromagnetic parameters Radar cross section

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Rapid quantification of grapeseed oil multiple adulterations using near-infrared spectroscopy coupled with a novel double ensemble modeling method

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024年 311卷 124016页

作者： Bian, Xihui Liu, Yuxia Zhang, Rongling Sun, Hao Liu, Peng Tan, Xiaoyao Tiangong Univ Sch Chem Engn & Technol State Key Lab Separat Membranes & Membrane Proc Tianjin 300387 Peoples R China Shandong Univ NMPA Key Lab Technol Res & Evaluat Drug Prod Jinan 250012 Peoples R China

As a high-quality edible oil, grapeseed oil is often adulterated with low-price/quality vegetable oils. A novel ensemble modeling method is proposed for quantitative analysis of grapeseed oil adulterations combined with near-infrared (NIR) spectroscopy. The method combines Monte Carlo (MC) sampling and whale optimization algorithm (WOA) to build numerous partial least squares (PLS) sub-models, named MC-WOA-PLS. A total of 80 adulterated grapeseed oil samples were prepared by mixing grapeseed oil with soybean oil, palm oil, cottonseed oil, and corn oil with the designed mass percentages. NIR spectra of the 80 samples were measured in a transmittance mode in the range of 12,000-4000 cm -1. Parameters in MC-WOA-PLS including the number of latent variables (LVs) in PLS, iteration number of WOA, whale number, number of PLS sub-models, and percentage of training subsets were optimized. To validate the prediction performance of the model, root mean square error of calibration (RMSEC), root mean square error of cross -validation (RMSECV), root mean squared error of prediction (RMSEP), correlation coefficient (R), residual predictive deviation (RPD), and standard deviation (S.D.) were used. Compared with PLS, standard normal variate-PLS (SNV-PLS), uninformative variable elimination-PLS (UVE-PLS), Monte Carlo uninformative variable elimination-PLS (MCUVE-PLS), randomization test-PLS (RTPLS), variable importance in projection-PLS (VIP-PLS), and WOA-PLS, MC-WOA-PLS achieves the best prediction accuracy and stability for quantification of the five pure oils in adulterated grapeseed oil samples.

关键词： Adulterated grapeseed oil Spectral analysis Multivariate calibration whale optimization algorithm Monte Carlo sampling

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Joint Task Offloading and Radio Resource Management in Stochastic MEC Systems

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IEEE TRANSACTIONS ON COMMUNICATIONS 2024年第5期72卷 2670-2686页

作者： Hoang, Thien Hieu Nguyen, Chuyen T. Do, Tri Nhu Kaddoum, Georges Univ Quebec Ecole Technol Super ETS Dept Elect Engn Montreal PQ H3C 1K3 Canada Hanoi Univ Sci & Technol Sch Elect & Elect Engn Hanoi Vietnam Polytech Montreal Dept Elect Engn Montreal PQ Canada

In this paper, we present a novel coexistence uplink-downlink stochastic mobile edge computing (MEC) system that considers the dynamic characteristics of both small cell base stations (SBSs) and user equipments (UEs). To devise an efficient radio resource management strategy encompassing user association, channel allocation, and power allocation, we formulate an optimization problem that considers time, energy, and achievable rate in the utility function. The formulated problem is a Mixed Integer Nonlinear Program (MINLP) and has been proven to be NP-hard. To address this complexity, we propose a unified nature-inspired optimization framework, which can be deployed for subproblems in various settings and can be integrated with the whale optimization algorithm (WOA), Improved whale optimization algorithm (IWOA), and Particle Swarm optimization (PSO). Through our rigorous mathematical and numerical analysis, the proposed algorithms show that they can converge to a near-optimal solution while keeping negligible optimality gaps. Our numerical results show the advantages and drawbacks of the proposed algorithms, highlighting their potential for effective resource management in MEC systems. The results also show the performance evaluation of stochastic characteristics on the performance of MEC.

关键词： Resource management Stochastic processes Heuristic algorithms Task analysis Servers optimization Mathematical models Mobile edge computing stochastic resource allocation multi-task offloading whale optimization algorithm

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